sbodenstein

"""Pseudocode description of the AlphaZero algorithm.
Original taken from 'Data S1' from https://science.sciencemag.org/content/362/6419/1140/tab-figures-data/
"""


from __future__ import google_type_annotations
from __future__ import division

import math
import numpy

sbodenstein

import numpy as np
import pyspiel
import time

def play_random_game(game):
    game_state = game.new_initial_state()
    while not game_state.is_terminal():
        random_action = np.random.choice(game_state.legal_actions())
        game_state.apply_action(random_action)

sbodenstein

// Build with with `-O` and `Whole Module optimization` flags

import Dispatch
import Foundation
import OpenSpiel

func playRandomGame<T: GameProtocol>(_ game: T) {
  var state = game.initialState
  while !state.isTerminal {
    state.apply(state.legalActions.randomElement()!)

sbodenstein

sbodenstein

-- 

require 'cudnn'
require 'cunn'

torch.setdefaulttensortype('torch.FloatTensor')

-- Get weight dim
function checkSums(rnn, seqLength, batch, inputDim, hiddenSize, layerNum, bidirectional)
	rnn:reset()